Forecasting Electricity Consumption in France Using a Hybrid Method Based on Artificial Intelligence and Statistical Approaches.

Mohamed Hamza Kermia, Konstantinos Aiwansedo, Oussama Djadane,Jérôme Bosche,Dhaker Abbes

International Conference on Systems and Control(2023)

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摘要
This work proposes an innovative hybrid method for accurate one-day electricity consumption forecasting. By combining artificial intelligence (AI) and statistical techniques, our hybrid model optimizes electricity production forecasting. Unlike methods used by RTE, our approach relies solely on consumption data, eliminating the need for additional variables. By preprocessing historical data and employing neural networks and statistical models, our hybrid method achieves exceptional accuracy, closely approaching RTE's performance, while significantly reducing data usage and computational demands. This promising approach streamlines electricity production planning, offering an efficient and environmentally friendly solution to environmental and economic challenges.
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关键词
Artificial Intelligence,Hybrid Method,Electricity Consumption,Consumption In France,Neural Network,Statistical Models,Prediction Methods,Statistical Techniques,Consumption Data,Hybrid Model,Forecast Accuracy,Electricity Production,Production Planning,Artificial Intelligence Techniques,Intelligence Techniques,Data Sources,Time Series,Statistical Methods,Energy Consumption,Convolutional Neural Network,Artificial Intelligence Models,Long Short-term Memory,Mean Absolute Error,Patterns In Data,Autoregressive Integrated Moving Average,Trend Component,Precise Prediction,Annual Consumption,Time Series Prediction,Time Series Data
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